Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?
Giovanni IrmiciMaurizio CèElena CaloroNatallia KhenkinaGianmarco Della PepaVelio AscentiCarlo MartinenghiSergio PapaGiancarlo OlivaMichaela CellinaPublished in: Diagnostics (Basel, Switzerland) (2023)
Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.
Keyphrases
- health information
- artificial intelligence
- healthcare
- machine learning
- big data
- low cost
- deep learning
- heart failure
- public health
- emergency department
- high resolution
- end stage renal disease
- clinical practice
- dual energy
- chronic kidney disease
- newly diagnosed
- adverse drug
- peritoneal dialysis
- spinal cord
- prognostic factors
- computed tomography
- left ventricular
- emergency medical
- stem cells
- intensive care unit
- bone marrow
- atrial fibrillation
- patient reported outcomes
- rna seq
- patient safety
- quality improvement
- mechanical ventilation
- respiratory failure